Notebooks

Part 5 of a multi-step data preparation pipeline, focusing on generating embeddings to transform structured and unstructured data into machine-learning-ready feature representations.

Part 4 of a multi-step data preparation pipeline, focusing on identifying, handling, and resolving missing values and data gaps to improve dataset reliability for machine learning.

Part 3 of a data preparation pipeline, focusing on preparing and enriching a cleaned dataset with ML- and AI-relevant fields to support downstream modelling and embeddings.

Part 2 of a multi-step data preparation pipeline, focusing on processing and standardising a combined project dataset to ensure consistency, correctness, and readiness for downstream analysis.

Part 6 and final step of a data preparation pipeline, focusing on validating dataset integrity, consistency, and readiness for machine learning workflows.

A Python notebook that prepares raw CSV data for machine learning by joining datasets, cleaning fields, and producing a structured training-ready dataset.

A Python notebook that generates cryptographic hashes from input data, introducing hashing concepts, repeatability, and basic security principles.

A Python notebook that introduces object-oriented programming concepts using simple animal classes, attributes, and methods.

A Python notebook that applies object-oriented programming concepts to build a simple library management system using classes and object interactions.

A Python notebook that enhances timesheet analysis by using proper datetime handling to improve accuracy in duration calculations and time-based insights.

A Python notebook that converts CSS color definitions into TypeScript-friendly objects, bridging design tokens and frontend development.

A Python notebook that converts structured JSON data into TypeScript-friendly objects, making large datasets easier to consume in modern frontend applications.

A concise Pandas cheatsheet covering common ways to drop rows, columns, and values from DataFrames using the drop method.

A Python notebook used to verify that a local Python environment is correctly installed and ready for development.

A Python notebook that implements a simple Tic Tac Toe game, demonstrating game logic, control flow, and state management.

A Python notebook that analyses timesheet and project tracking data to uncover work patterns, time allocation, and productivity insights.

A hands-on Python notebook that demonstrates real-world data processing using CSV files, structured data cleaning, and basic aggregation techniques.

A step-by-step Python tutorial that builds a simple library management system, demonstrating collections, functions, and state management.

A beginner-friendly Python tutorial covering core language fundamentals such as variables, data types, input, and basic operations.

A Python notebook that refactors a calculator into reusable modules, demonstrating separation of concerns, operator mapping, and cleaner program structure.

A Python notebook that introduces external modules and NumPy, demonstrating how Python is extended with powerful libraries for numerical computation.

A Python Jupyter Notebook that implements a simple calculator using functions, operator handling, and basic error management.

A Python notebook that simulates a simple library management system, introducing state, collections, and basic CRUD-style operations.

An expanded Python notebook that builds on basic arithmetic by introducing functions, validation, and more structured logic for calculating fruit totals.

A beginner-friendly Python notebook that introduces conditional logic and decision-making using simple fruit selection scenarios.

A beginner-friendly Python notebook that demonstrates user input, type casting, and a common pitfall when working with strings and numbers.

A simple Python notebook that introduces lists and sorting, showing how data can be organized programmatically in a clear and beginner-friendly way.